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Computer Science
Data structures & algorithms, rebuilt
Arrays, trees, graphs, and the Big-O intuition behind them — not a LeetCode grind, a mental model.
8 lessons
~140 min total
First principles
What you'll learn
Choose the right data structure on instinct — arrays, hash maps, heaps, trees, graphs
Reason about time and space complexity without memorizing tables
Recognize the handful of algorithmic patterns that solve 90% of problems
Progress
0 / 8
Track complete ✓
Lessons
1
Big-O, intuitively
Counting operations, not stopwatches — and why constants don't matter.
3 objectives
2
Arrays and hash maps
The two containers that solve an unreasonable fraction of problems.
3 objectives
3
Stacks, queues, and why order matters
LIFO vs FIFO — and the surprising range of problems each unlocks.
3 objectives
4
Trees and recursion
Binary trees, BSTs, and why so many problems are just recursion in disguise.
3 objectives
5
Heaps & priority queues
The structure you reach for when 'top-K' or 'next smallest' shows up.
3 objectives
6
Graphs: BFS, DFS, and the rest
Most 'grid' and 'network' problems are graph problems in cheap clothing.
3 objectives
7
Dynamic programming, demystified
Overlapping subproblems + optimal substructure — everything else is bookkeeping.
3 objectives
8
The dozen patterns that actually show up
Two pointers, sliding window, binary search, and the other reliable shapes.
3 objectives
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